As 6G networks must support diverse applications with heterogeneous quality-of-service requirements, efficient allocation of limited network resources becomes important. This paper addresses the critical challenge of user admission control in 6G networks enhanced by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). We propose an optimization framework that leverages RIS technology to enhance user admission based on spatial characteristics, priority levels, and resource constraints. Our approach first filters users based on angular alignment with RIS reflection directions, then constructs priority queues considering service requirements and arrival times, and finally performs user grouping to maximize RIS resource utilization. The proposed algorithm incorporates a utility function that balances Quality of Service (QoS) performance, RIS utilization, and MEC efficiency in admission decisions. Simulation results demonstrate that our approach significantly improves system performance with RIS-enhanced configurations. For high-priority eURLLC services, our method maintains over 90% admission rates even at maximum load, ensuring mission-critical applications receive guaranteed service quality.

Named Data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availability, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

In wireless sensor networks (WSNs), energy constraint is a long-standing problem. With the development of energy-harvesting technology, energy-harvesting wireless sensor networks (EH-WSNs) have emerged. However, energy-harvesting wireless sensor nodes can be affected by their factors or external factors, making the energy collected by the nodes unequal. When individual nodes run out of energy in advance, the data will not be forwarded to the next hop node normally, thus generating data accumulation. To address this problem, this paper proposes a packet priority-based MAC protocol (DPP-MAC) for EH-WSN, which consists of two main parts: (1) Research on the channel allocation algorithm based on the remaining energy of nodes to avoid data accumulation during the transmission of data by low- energy nodes; (2) Stacking data transmission algorithm based on packet priority to solve the problem of node data stacking and improve network performance. By comparing with the existing MAC algorithm in terms of network throughput packet loss rate, average end-to-end delay, and channel utilization, it is found that the proposed DPP-MAC improves the network performance and better solves the data buildup problem.

The traditional particle swarm optimization (PSO) algorithm suffers from shortcomings like easily falling into local optimum and inadequate sharing of information among particles, To ad-dress these limitations and enhance the search capacity of the particle swarm algorithm, we present a novel particle swarm optimization algorithm known as Exponential Dynamic Inertia Weight for Particle Swarm Optimization (ExDyPSO) in this paper. ExDyPSO is composed of two parts: firstly, by introducing dynamic inertia weight based on exponential distributions and acceleration factors that vary with the number of iterations, harmonizes the global and local search capabilities. Secondly, a stochastic particle-based jump-out strategy is proposed to surmount the case of particles falling into stagnation during the search process, thus effectively addressing the issue that PSO is prone to falling into local optimum. To assess the performance of ExDyPSO, we carried out experiments on eight benchmark functions and compared its performance against four alternative PSO variations. The experimental findings demonstrate that ExDyPSO achieves quicker and more accurate convergence towards the global optimal solution, all the while sustaining population diversity.

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all available links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of available network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

In recent years, with the increasing popularity of Internet technology and the advent of the 5G communication era, the applications related to mobile IoT have exploded, and wireless Ad Hoc network has received more and more attention. In traditional Ad Hoc networks, data are often transmitted between nodes in the form of "multi-hops", and the transmission range and available energy of nodes are limited. Thus, in Ad Hoc networks, a reliable and energy-efficient data transmission protocol is needed. In this paper, we studied the existing multipath routing protocols and proposed a multiple performances based routing protocol E2LR, ( residual Energy, Energy consumption, Loop, Routing protocol ) for the shortcomings of Ad Hoc networks. This protocol has the following improvements to the existing multi-path routing protocols: (1) In the route discovery process, depending on the energy level of the nodes, the nodes make three different decisions on the received RREQ packets: discard, randomly delayed forwarding, and immediate forwarding, to filter out the nodes with lower energy and thus improve the network performance. (2) When selecting available paths, try to select nodes whose lifetime are close to each other. This can reduce the number of interruptions in the transmission path and improve the stability of the transmission path during data transmission. (3) When transmitting data, multiple paths stored in the source node are recycled to transmit data, thus balancing the node energy. Simulation results show that the E2LR routing protocol can effectively reduce the frequency of route initiation, shorten the data transmission delay, reduce the packet loss rate in the network compared with existing multipath routing protocols.

The rapid evolution of mobile networks presents challenges for devices with limited computing power. Mobile or multi-access edge computing (MEC) addresses this by providing computing resources in proximity to end devices. However, MEC servers face constraints in resource sharing, necessitating efficient allocation. We propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. Tasks are prioritized based on type, size, and maximum tolerable delay. Different offloading algorithms are applied for varying priority tasks, considering current server load. Simulation results demonstrate BOMT’s effectiveness in reducing latency, enhancing user coverage, and improving task completion rates.

Research on Energy Harvesting Wireless Sensor Networks (EH-WSN) has received much attention in recent years. However, in practical applications, due to the forced movement of sensor nodes in the deployment environment or the randomness of deployment, the dense and sparse distribution areas of nodes are formed in the monitoring area, and there may also exist isolated nodes. In the dense region, data conflicts are easily generated between nodes, while in the sparse region, the connectivity between nodes is low, the packet loss rate becomes high, and the data of isolated nodes cannot be transmitted normally. To address the above problems, this study proposes an adaptive MAC protocol in EH-WSN in harsh environments. This paper proposes an adaptive MAC protocol for EH-WSN in harsh environments, (AHE-MAC). The main idea of AHE-MAC is that: for the uneven distribution of nodes in harsh environments, this research firstly categorizes the node deployment area into “dense area and sparse area”. Secondly, different data transmission methods are proposed for different regions; in the dense region, the nodes implement the dynamic sleep mechanism, which puts some nodes to sleep and other nodes transmit the data to the next hop nodes, thus reducing the data transmission conflicts. In the sparse region, the isolated nodes adopt the “tentative power increasing“ mechanism to transmit data packets, and the other sparse nodes adopt the improved TDMA algorithm to transmit data, thus increasing the connectivity of the nodes. The simulation results prove that the protocol ensures the effective transmission of data in different network areas and improves the network performance at the same time.

Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a "backup cluster head" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users’ delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.

Online public transit ridership information is helpful to enhance the service quality of urban public transportation and the travel experiences of passengers. Passive WiFi sensing collects WiFi probe (request) frames sent by nearby mobile devices in a non-intrusive manner, and can thus be employed to monitor ridership. Compared with the existing non-WiFi based approaches, passive WiFi sensing based approaches demonstrate the advantages of limited interferences, large coverage, low costs and lightweight calculations. More recently, although some dedicated passive WiFi sensing based methods have been proposed in an offline mode, due to sniffing opportunistically, unknown dynamic transmission boundary, MAC randomization and the difficulty in online feature extraction, how to utilize limited sensing data to provide accurate online ridership information is still challenging. To this end, an innovative public transit ridership monitoring system built upon a customized WiFi sniffer and an online ridership estimation algorithm is developed. In the algorithm, a convolutional neural network (CNN) module and a bidirectional long short-term memory (BiLSTM) neural network module are first adopted to find correlations among inputs and capture the bidirectional time-series features, respectively; furthermore, an attention module is incorporated to determine the importance of an input sequence at different times. Real-world experiments are carried out on 8 buses corresponding to 4 bus routes in Hohhot, China. The evaluation results show that the proposed algorithm outperforms the other 4 online algorithms and the state-of-the-art offline algorithm.

Abstract Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy consumption of such a popular service. Therefore, this paper focuses on the energy efficiency of step-counting algorithms. First of all, we formulate a theoretical error model based on the well-known auto-correlation coefficient step-counting (ACSC) algorithm, so as to analyze the factors affecting step-counting accuracy. And then, in light of this model and an adaptive sampling strategy, we propose a novel energy-efficient step-counting algorithm by adaptively substituting the computationally intensive auto-correlation with simple mean absolute deviation. On these grounds, an Android pedometer is implemented. Two individual experiments are carried out and verify both the theoretical error model and the proposed algorithm. It is shown that our algorithm outperforms two famous counterparts, i.e. the original ACSC algorithm and peak detection step-counting algorithm, in terms of both accuracy and energy efficiency.

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning technologies had emerged in the past decades, and WiFi-based approach is one of the most promising ones. However, the existing WiFi localization methods fail to take into account the disparate influence of packets transmitted in different channels so as to inhibit the further improvement of localization accuracy. Therefore, we present CADNN: a Channel Adaptive WiFi localization method based on Deep Neural Network (DNN). Specifically, a comprehensive analysis on signal attenuations in different channels along with error analysis based on Cramer-Rao Lower Bound (CRLB) is conducted in theory. Then, the channel set splitting scheme for practical localization to leverage multi-channel features is proposed. Finally, we design a localization framework using multi-objective regression DNN to adapt Received Signal Strength (RSS) measurements from different channel sets. The results from real-world experiments confirm the effectiveness of channel adaptive and show that CADNN can improve localization accuracy by at least 25.3% and 19.5% respectively on the two datasets and it can serve thousands users within one second.

Energy Harvesting Wireless Sensor Networks (EHWSNs) have been widely studied in recent years. In solar charged EH-WSNs, the Sun illumination changes with the changes of environment, in consequence the collected energy of the sensor node is unstable, especially in rainy day, windy day or the angle of the solar panel changes. Therefore, the reasonable assignment of energy in EH-WSNs becomes critical important. In This paper, based on the solar energy charateristics, we propose a priority task scheduling algorithm that suitable for EH-WSNs, that is, the transmission method and order of collected data are determined according to task priority and the remaining energy of the node. The simulation results show that the priority task scheduling algorithm guarantees the fairness of node energy distribution, the timeliness of sending urgent tasks and the high processing rate of common tasks when the energy provided by the environment is small.

WiFi fingerprint-based localization is one of the most attractive and promising techniques targeted for indoor localization, and has attained much attention in the past decades. In addition to improving localization accuracy, various efforts have been devoted to efficiently building a radio map which is normally tedious and laborious. Therefore, this paper proposes an efficient approach for building compact radio maps based on compressed multivariate kernel density estimation (CMKDE), in the sense that only a few received signal strength (RSS) measurements are required and the resulting radio maps are far less than the sizes of traditional radio maps. Extensive experiments are carried out in a real scenario of nearly 1000 m2during several working days, and a comparison is made with two existing popular solutions including the Gaussian process regression (GPR) and another approach based on kernel density. It is shown that the proposed method outperforms its counterparts in terms of both robustness and accuracy.

Frequent itemset mining has been a focused theme in the field of data mining, which is widely used in business decision making, economics, medicine, bioinformatics and other fields. Frequent itemset mining can provide a lot of valuable information when making decisions, but it may bring the risk of privacy disclosure when mining and publishing frequent itemsets. In order to solve the privacy leakage problem, most of the existing solutions are using horizontal mining method to mine frequent itemsets under differential privacy. However, these solutions generally suffer from complex support computation and poor accuracy due to large candidate sets. In this paper, we propose a new vertical frequent itemset mining algorithm based on differential privacy, which is referred to as DP-Eclat. In DP-Eclat, a new privacy budget allocation strategy is proposed to rationalize the privacy budget allocation, which allows privacy budget to be used more fully. In addition, we devise a multiple pruning strategy to further improve the data utility by prune before and after the generation of candidate itemsets. Through privacy analysis, we prove that DP-Eclat satisfies E -differential privacy. Extensive experiment results on multiple real datasets show that DP-Eclat significantly outperforms state-of-the-art algorithms in terms of data utility.

Privacy-preserving data publishing is a hot issue in the field of privacy protection. Differential privacy is a burgeoning technology of privacy protection which provides a powerful privacy mechanism and does not make restrictive assumptions on the attacker's background knowledge. At present, there does not have an effective way to generate Synthetic high-dimensional data with differential privacy technology. Aiming at the issue of high-dimensional privacy data publishing, this paper proposed a method called APrivBayes, which altered the structure of Bayesian network to make it adapting differential privacy mechanism. Then proposed a first node selection mechanism based on attribute correlation degree for the new structure of Bayesian network. Through theoretical analysis and experimental evaluation, this method improves the effect of network and reduces Laplace noise effectively, while protecting personal privacy and improving the usability of published data.

With the rapid development of the Internet and combined with outsourcing, a new paradigm - crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers' bidding.

The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.

In this paper, a novel scheme is reported to adapt radio maps to environmental dynamics in an online fashion by combining crowdsourcing and gaussian process regression (GPR). Specifically, a Marginalized Particle Gaussian Process (MPGP) is adopted to recursively fuse crowdsourced fingerprints with an existing offline radio map. The advantages of the proposed scheme lie in the efficiency and scalability in comparison with the traditional approaches. Extensive experiments are carried out in a real scenario of nearly 1000 m2during five months, and a comparison is made with several existing popular solutions. It is shown that the proposed scheme outperforms its counterparts in terms of both robustness and accuracy.

Mobile Crowd Sensing Network (MCSN) is a new sensing mode in the Internet of Things by taking advantage of individual's mobile device with multiple sensors to collect some specific data for certain applications. There are two modes of transmission mechanism in the existing systems: one is end-to-end mode via the cellular network and the other is opportunistic transmission via short-range wireless communication technology. Due to the higher cost, the former is not conducive to enlarging users'participation. This paper focuses on the mode of the latter to build a new opportunistic transmission in MCSN. Differently from most existing studies, i.e. preference-aware and energy-aware, this paper proposes an opportunistic data transmission mechanism based on SSIS model (a socialization SIS epidemic model), who can transfer the sensing data to the platform by forwarding step by step without extra cost. Specifically, firstly, SSIS model is defined based on SIS epidemic model by social information in MCSN. Additionally, SSIS model is used to analyze the social relationship of mobile sensing nodes in MCSN to obtain a social relational table. Finally, the social relational table is used to improve the Spray and Wait (SW), which is a typical opportunistic transmission mechanism, to guide the source node which performs the task of sensing to propagate the sensing data selectively to other nodes until it reaches the destination node which can send the data to the platform. Simulation results show that the performance of data transmission can be further improved by using the proposed mechanism in comparison with SW, Epidemic and Prophet.

Acquiring the knowledge of WiFi access point (AP) locations not only plays a vital role in various WiFi related applications, such WiFi-based indoor localization, the deployment of new WiFi APs, and so on, but also contributes to the emergence of novel applications. Most existing studies assume the well-known lognormal shadowing model, which only reflects large-scale fading in WiFi signal propagations but ignores small-scale fading induced by pervasive multipath effects. In this paper, we tackle the problem of AP localization based on the Rayleigh lognormal model which characterizes the influence of both large-scale and small-scale fading. Provided that a participant holding a smartphone is walking along a path and the smartphone automatically and continuously collects received signal strength (RSS) measurements from a target AP at known positions, particle filtering is applied to sequentially narrow the scope of possible locations of as well as the propagation parameters of the wireless signals emitted by the target AP, and the weighted mean of all candidate locations is returned as its final location estimate. Extensive experiments are carried out in typical indoor and outdoor scenarios, and reveal that the proposed method outperforms the solutions based on the lognormal model by 14.13%-70.38%.

Radio maps play a vital role in fingerprint-based indoor positioning systems (IPSs) in terms of the localization accuracy and computational overheads. Most existing studies either directly eliminate redundant APs or adopt unsupervised dimension reduction methods, say principal component analysis (PCA), to obtain a low-dimension representation of fingerprints, which consumes less storage and computational overheads. In this paper, we propose to reduce the dimensions of radio maps based on the Gaussian Process Manifold Kernel Dimension Reduction (GPMKDR) which is a supervised dimension reduction technique in comparison with the well known PCA-based method. Specifically, GPMKDR is employed to find a nonlinear and optimal embedding into the received signal strength (RSS) sample space during the offline phase, such that any RSS sample vector obtained in the online localization phase can be projected onto the optimal subspace with a lower dimension, with the result that the fingerprint-based localization can be efficiently realized based on a low-dimension radio map. Experiments show that the nonlinear GPMKDR-based method significantly improves the localization performance in comparison with the PCA-based method.

The Internet of Things aims to provide the user with deep adaptive intelligence services according to the user’s personalized characteristics. Most of the characteristics are presented in the form of high-level context. But it often lacks methods to obtain high-level context information directly in the Internet of Things. In this paper, so as to achieve the corresponding high-level context information using the specific low-level multidomain context directly obtained by different sensors in the Internet of Things, we present a machine learning method to construct a context fusion model based on the feature selection algorithm and the multiclassification algorithm. First, we propose a wrapper feature selection method based on the genetic algorithm to obtain a simpler and more important subset of the context features from the low-level multidomain context, by defining a suitable fitness function and a convergence condition. Then, we use the decision tree algorithm which is a multiclassification algorithm, based on the rules obtained by training the subset of context features, to determine which high-level context the record set of the low-level context information belongs to. Experiments confirm that the model can be used to achieve higher classification accuracy without more significant time consumption.

Optimizing overall performance of Wireless Sensor Networks (WSNs) is important due to the limited resources available to nodes. Several aspects of this optimization problem have been studied (e.g. improving Medium Access Control (MAC) protocols, routing, energy management) mostly separately, although there is a strong inter-connection between them. In this paper an Artificial Intelligence (AI) based framework is presented to address this problem. Mixed-Observability Markov Decision Processes (MOMDPs) are used to effectively model multiple aspects of WSNs in stochastic environments including MAC in data link layer, routing in network layer, data aggregation, power management, etc. MOMDPs distinguish between full and partial observability, hence they are more efficient than other similar AI methods. The proposed framework provides global optimization of user-defined performance metrics, e.g. minimization of time delay, energy consumption and data inaccuracy. Near-optimal joint network policies are obtained via offline approximation of optimal MOMDP solutions and they are distributed among the individual nodes. Resulting node-policies place effectively no additional computational overhead on nodes in runtime. Experiments evaluate the framework by demonstrating near-optimal solutions for a small-scale WSN in detail in case of given tradeoff criteria. The proposed approach produces better joint network behavior in 5 out of 6 cases compared to other two standard methods in simulation by increasing overall network performance by more than 20% in average.

Due to the Recent development in wireless technology, wireless sensor networks attract researchers' attention because of their applicability in many fields for effective collection of sensing data with low cost. Wireless sensor networks have many applications; some of the applications are military application, environmental application and flood detection. For example, in an environmental application for forest fire detection, the sensor nodes sense the fire information, then transmit or relay the information to base station in a multi-hop way. In wireless sensor networks, energy saving is critical issue as sensor nodes are battery-powered. Here we propose, partial data aggregation as one of the energy saving technique. In this paper, we analyze the tradeoffs among communication delay, energy consumption, and data accuracy of the partial data aggregation technique and discuss the results. First, we analyze the partial data aggregation with Markovian chain; analytical result shows that, non-aggregation method suffers large energy consumption while full aggregation suffers long transmission delay. From the analysis results, we find that the proposed partial aggregation method WRP (Waterfalls Random partial aggregation) can trade off energy consumption and transmission delay. Thus, we discuss the tradeoffs among data accuracy, transmission delay and energy consumption with different criteria and parameters. The results show that we could control the significance of transmission delay, energy consumption and data accuracy by tradeoffs index (TOI). We also analyze the several applications of wireless sensor networks with different significance based on the TOI. From the observed results, we found that we could set the significance of transmission delay, energy consumption and data accuracy for different applications based on different criteria TOI. Thus, by evaluating and comparing the criteria with different data generation rate as well as aggregation factor, we get the least TOI value, which denotes the desired tradeoffs among them.